Dynamic delegation with a persistent state
研究了委托人如何根据代理人报告的持续演变状态动态授权，发现最优授权有时会建议与目标相反的行动，并给出了发生这种情况的充要条件。
In this paper, I study the dynamic delegation problem in a principal–agent model wherein an agent privately observes a persistently evolving state, and the principal commits to actions based on the agent's reported state. There are no transfers. While the agent has state‐independent preferences, the principal wants to match a state‐dependent target. I solve the optimal delegation in closed form, which sometimes prescribes actions that move in the opposite direction of the target. I provide a simple necessary and sufficient condition for that to occur. Generically, the principal fares strictly better in the optimal delegation than in the babbling outcome. Over time, the principal is worse off in expectation, but the agent is better or worse off depending on the shape of the principal's state‐dependent target.